Enterprise AI Adoption
Fewer AI pilots. More AI in production.
InfoMover helps companies decide where Gen AI is worth adopting and how to get it into production safely. We review your processes, data and systems, rank use cases by value and risk, prove the strongest one in code, and set the governance, architecture and roadmap that let the next ones follow without starting over.
Sound familiar?
- Every department wants an AI pilot, and nobody can say which ones matter.
- Pilots impress in the demo and stall before production.
- Security and legal block AI because nobody has written down the rules.
- Each team picks its own model, vendor and approach, and nothing is reused.
What you get
- Use cases, rankedCandidate use cases scored on value, feasibility, data readiness and risk, with a clear first choice.
- One use case proved in codeA working proof of concept on your own data, so the roadmap starts from evidence, not slides.
- Rules people can followPolicies for data, models, vendors, human review and audit, written with your security and legal teams.
- A shared foundationA reference architecture and a roadmap, so the second and third use cases reuse what the first one built.
Ways to engage
Pick the shape that fits.
Most adoption work starts with an advisory sprint to rank the use cases, then a proof of concept on the first one.
Built by us
Projekts.ai
We designed and built Projekts.ai, our AI-native software engineering platform, so the adoption advice we give comes from taking Gen AI to production ourselves.
Visit Projekts.ai ↗Prefer to learn it?
GenAI Design & Architecture — Engineering Robust Enterprise AI Systems
A hands-on Gen AI design and architecture workshop for the engineers who will build what comes next.
See the course ↗Questions & answers
Where should a company start with Gen AI?
With one use case that matters to the business and has the data to support it. An Advisory Sprint ranks the candidates and picks the first one; a Proof of Concept then shows whether it works on your data.
Do you recommend a particular AI vendor?
No. We compare models and platforms per use case, on quality, cost and where your data is allowed to go, and design so you can change vendor later.
How do you handle data privacy and governance?
We write the rules with your security and legal teams before anything goes live: which data a model may see, who reviews its output, and what is logged for audit.
Can our own teams carry on after you leave?
Yes, that is the aim. We leave a reference architecture, a roadmap and working code, and hands-on training for your engineers is available through our training platform, Workshopps.
Start a conversation
Talk to Shakir about Enterprise AI Adoption.
Tell us where your company is with AI today and what is holding it back. We'll suggest the first use case to prove.